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Intro to Data Analysis

Utah Valley University - BIOL3100

Handy links:

Course Syllabus

Course file repository

Shared Course Notes

(Anyone with link can edit)

Exams GitHub Repository (Exams will be uploaded at the appropriate times)

The philosophy of this course

R for Data Science Website

Collection of beginner R resources

Amy Willis’ Intro to R course

(for related alternative exercises/lessons)

Big Book of R collection of free R books …Whoa!

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The Command Line, File Paths, Git

Week 1

Topics:

• Installing Software | Command-line | Git version control

Assignments

• Read: What is Git all about?
• Install Git, R, and R-Studio on your laptop (part of Assignment 1)
• Be ready to explain what Git, R, and R-Studio are.
• Do Assignment 1 and upload a link to your new GitHub account to Canvas.
• Take a look at this document to see where this class is going
• Go through ALL the resources below. I put them here for a reason. Most are short web resources or videos (some that I made).

Resources

• Recorded lesson video Part 1
• Recorded lesson video Part 2
• Video: Meet the command line of your computer
• Download Git
• Download R
• Download R-Studio
• Navigating with the command line (great video!)
• GitHub steps for Assignment 1 video
• Git Cheat Sheet (handy reference)
• Git tutorial for beginners (another walkthrough, if you need it)
• Best Git Cheat Sheet Ever! (Excellent gift idea for a teacher…joking!)

Practice

• Make 10 more separate changes and commits to your README.md file and push each one to GitHub
• Close and open your command line terminal 10 times
• Open your command line terminal and navigate to your new personal GitHub repository for this course (Data_Course_LASTNAME) / Navigate back to your desktop / From your Desktop (without using “cd”) display the contents of Data_Course_LASTNAME/README.md onto your computer screen.
• Please view this short video clip from “Karate Kid” (Seriously)
  • When I tell you to close and open your command line 10 times, it’s not because I hate you.
  • It’s because I, too, have had to learn this stuff from scratch
  • It’s because I know that repetition is crucial to learning this, especially at the beginning
  • And it’s because if you don’t spend the time to do this stuff over and over now, by week 6 you will be drowning and helpless.
  • When I say “push 10 separate commits to your GitHub repo,” what I’m actually saying is “Show me ‘Paint the Fence’!”
  • Because very soon, Mr. Miyagi will be attacking you with things like “Error in url[i] = paste(df[,2], gsub(” “,”_“, : object of type ‘closure’ is not subsettable”

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Week 2

Topics

• File paths | Wildcards and pattern matching | Objects | For-Loops

Assignments

• Assignment 2

Resources

• Remedial Unix Shell
• Basic Unix Commands
• Very Useful Tutorial
• On the Value of Command-Line Bullshittery
• On the Annoyance of Command-Line Bullshittery
• Video walkthroughs of some command line stuff:
  • Part 1 - first commands
  • Part 2 - pipes and wildcards
  • Part 3 - relative filepaths
  • Command line program flags/parameters
  • How to avoid two potentially dangerous command line errors
  • For-loops video walkthrough in BASH
• Bonus tips:
  • BASH aliases video
  • BASH alias examples

Practice

• In the directory Data_Course/Data/data-shell/names/ there are a number of subdirectories and csv files. Find all of those csv files and store their full filepaths as a character vector.
• Read in and print just the first 2 lines from each of those files
• Find all the .txt files on your entire computer
• Find all files on your computer that contain the character string “es” in the filename

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Getting to Know R

Week 3

Topics

• R Data types and conversions | Reading and Writing Files | Packages and Projects

Assignments

• Read this chapter on what a “package” is in R
• Read this chapter on R-Projects (We will ALWAYS work from within R-Projects from now on)
• Do Assignment 3 (We will start this one together during class)

Resources

• Intro to what RStudio is video
• Data Types in R
• Operators
• Subsetting
• More Subsetting (It’s important!)
• For-Loops
• Reading data into R
• Using ‘pipes’ in R chapter
• For-loops in R chapter

Practice

• Vectors
• Factors
• Characters
• Regular Sequences
• Indexing
• Missing Values
• Loops in R
• Logical Operations
• Find a new built-in data set in R. Use several methods to subset it over and over until you are an expert!
• Out-of-order Code

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Visualizing a Data Set

Week 4

Topics

• “Grammar of Graphics” ggplot | dplyr verbs

Assignments

• Read through the materials in the Resources section below
• Make sure you have the following packages installed if you don’t already:
  • tidyverse
• Do Assignment 4

• Exam 1 (Link at top of page)

Resources

• ggplot Introduction
• dplyr Verbs
• tidyverse Cheat Sheet
• More ggplot
• Extensive ggplot2 Tutorial
• How to plot anything in ggplot part 1 | part 2 (This is a GOOD thing to watch!)
• Evolution of a ggplot tutorial
• Catalog of visualization types (Awesome source of inspiration for your plots…but memberhip fee required to see code. Booooo)

Practice

• Out-of-order Plotting Code
• ggplot Shiny App Lets you use GUI to see ggplot code.
• Convert the following code expressions into “pipe format” to make them more readable:

unique(stringr::str_to_title(iris$Species))
max(round(iris$Sepal.Length),0)
mean(abs(rnorm(100,0,5)))
median(round(seq(1,100,0.01),1))

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Week 5

Topics

• More ggplot | ggplot extensions

Assignments

• Assignment 5 - Ugly plot contest!
• Prevent embarrassment… see how NOT to make a chart, except for the Ugly Plot Contest, of course, where you should try to upend good sense.

Resources

• ggimage package
• ggforce package
• patchwork package
• ggpubr package
• gganimate package

Practice

• ggplot Examples

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Clean and Transform Data

Week 6

Topics

• Tidy Data | dplyr verbs | tidyr verbs

Assignments

• Read this paper: Tidy Data

• Assignment 6

Resources

• Recorded lesson video Part 1
• Recorded lesson video Part 2
• Tidy Data Chapter
• Transforming Data Frames
• tidyr Verbs

Practice

• BioLog Challenge
• Out-of-order Code
• Out-of-order Code2

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Week 7

Topics

• Data Wrangling | Joins | The Curse of Other Peoples’ Data

Assignments

• Read This Handout

• Read This Paper

• Create a validated Excel Spreadsheet for data collection for the experiment we design in class

• Exam 2 is open…

• Assignment 7

Resources

• Recorded lesson video Part 1 (dplyr verbs)
• Recorded lesson video Part 2 (dplyr verbs in action)
• Data Wrangling Chapter
• Wrangling Cheat Sheet
• Visual Explanations of Joins
• Janitor package on CRAN
• Rstats illustrations
• Working with strings and regular expressions using the stringr package
• How Excel actually killed people news article

Practice

• Download this spreadsheet. See if you figure out all the things wrong with it.
• Error Sleuth Practice
• Data Entry Case Study

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Getting More From R

Week 8

Topics

• Writing Functions | Conditional Execution | source()

Assignments

• Watch this video from Jenny Bryan about debugging
• Read this chapter and do all the exercises in it as you read

• Exam 2 (Link at top of page)

Resources

• Recorded lesson video Part 1 (Designing an experiment)
• Recorded lesson video Part 2 (Analysis of experiment data - live coding)
• Functions
• Conditional Execution
• Functionals and the purrr package chapter

Practice

• Write a function that returns the min, max, and mean of any set of real numbers
• Write a function that takes a data frame and returns a new data frame with one random column removed
• Fix my out-of-order code for a summarizing function
• Write a function that takes a data frame… if there are more than 3 columns, your function should return the column names as-is; if there are 3 or fewer columns, your function should return the column names in reverse order.
• Write a useful function that you might want to use in the future (your choice)
• Put all of these functions into a new R script and save it in your main data course repository
• In a new empty R script, call your functions with source() and test them out
• There’s a stupid function I wrote in “/Code_Examples/thlayli.R”
  • It takes a data.frame as an input and does WHAT to it?
• What’s the difference between ‘|’ and ‘||’ or ‘&’ and ‘&&’ ? Why should you always use the double-form in a conditional expression?

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Model Building and Testing

Week 9

Topics

• Building and Testing Models

Assignments

• Assignment 8

Resources

• Recorded lesson video (Part 1 - Intro to linear models)
• What is a statistical model?
• Modeling Intro
• Model Basics
• Model Fitting
• Model Performance
• Interpreting models with easystats

Practice

• Building Models Example
• Intro to Hypothesis Testing
• Cross-Validation and Logistic Regression

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Week 10

Topics

• More models | Statistical Tests

Assignments

• Show up to class. Models are confusing at first and there’s a lot to learn.
• Ask questions during class.

Resources

• Recorded lesson video (Recorded during class and posted after)
• Linear Regression
• More Linear Regression
• Common Statistical Tests
• Most stats test are really just linear regression models!
• Mixed-effect Models
• Everything is a Regression
• Introduction to mixed effects models This is a VERY good paper!
• How to use lmer
• Comparing multiple models with regression tables
• Intro to Machine Learning online text
• Free case studies using Machine Learning in R
• Awesome student-made repository featuring good explanations of different models

Practice

• Go through the R script more_models.R
  • Follow along with my analyses of the first two data sets
  • Complete an analysis of the third data set

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Communicating Your Results

Week 11

Topics

• R-Markdown | Reproducible Reports

Assignments

• Assignment 9

Resources

• Recorded lesson video (Part 1)
• Recorded lesson video (Part 2)
• Intro to R Markdown
• Markdown Live Preview Generator
• Expert-Level Markdown Project
• Example data analysis (easy version) webpage

Practice

• Using the resources above, generate a markdown document that analyzes the “iris” data set and push it to a new GitHub repository named Iris_Markdown
• Play with options and code to create a document that looks good and presents your analysis and results clearly
• This is similar to Assignment_9, but I’m asking for a brand new “Iris_Markdown” repository that is a self-contained report of Iris analyses

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Week 12

Topics

• Proper Project Organization | Collaboration

Assignments

• Peer evaluation of Assignment 9 HTML reports (Organization, Portability, Accuracy, Understandablity)

• Exam 3 (Link at top of page)

Resources

• Recorded lesson video (Part 1)
• Project-oriented Workflows
• Reproducible, Portable, Self-Contained
• Proper Project Organization Example
• Another, More Detailed Example
• Project organization part 1 video
• Project organization part 2 video

Practice

• Peer evaluations of Iris_Markdown repositories (from last week); Clean them up and make them more organized

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Putting it all together

Week 13

Topics

• Data Analysis from raw to report

Assignments

• We will work together in class to do a complete analysis in real-time

Resources

• Recorded lesson video (Recorded during class and posted after)
• Benefits of Live Coding

Practice

• Analyze the “esoph” data set and generate a markdown report

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Week 14

Topics

• Building a website with GitHub and R-Markdown

Assignments

• Work on Final Project
• Create a GitHub Personal Website
• Upload a brief CV and the updated (improved) html of Assignment 9 to your new website

Resources

• Recorded lesson video (Recorded during class and posted after)
• GitHub Pages
• Here’s the GitHub repository for this course website
• Rmarkdown to web page walkthrough video
• Reproducible workflow video

Practice

• Go through my course website repository (link above) and try to relate the code there to the html version of the website your internet browser displays
• Work on your personal website:
  • Add multiple pages with internal links
  • Be sure to have a “Projects” page that links to HTML reports you’ve made, including your final project
  • Be careful not to push any files larger than 50Mb to GitHub or it will break your repository!

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Week 15

Topics

• Intro to genetic data in R

Assignments

• Work on Final Project
• Assignment 10 (Draft of final code)

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Week 16

Topics

• TBD

Assignments

• Exam 4 (Redo any previous exam to replace it’s score)

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‘Luck is statistics taken personally.’ – Penn Jillette